Chapter 4 Applications of Monte Carlo Simulation in Modelling of Biochemical Processes
The biochemical models describing complex and dynamic metabolic systems are typically multi-parametric and non-linear, thus the identification of their parameters requires nonlinear regression analysis of the experimental data. The stochastic nature of the experimental samples poses the necessity...
Tallennettuna:
| Päätekijät: | , , , , |
|---|---|
| Aineistotyyppi: | Online |
| Kieli: | englanti |
| Julkaistu: |
InTechOpen
2021
|
| Aiheet: | |
| Linkit: | 627382 |
| Tagit: |
Ei tageja, Lisää ensimmäinen tagi!
|
| Yhteenveto: | The biochemical models describing complex and dynamic metabolic systems are typically multi-parametric and non-linear, thus the identification of their parameters requires nonlinear
regression analysis of the experimental data. The stochastic nature of the experimental
samples poses the necessity to estimate not only the values fitting best to the model, but also
the distribution of the parameters, and to test statistical hypotheses about the values of these
parameters. In such situations the application of analytical models for parameter
distributions is totally inappropriate because their assumptions are not applicable for
intrinsically non-linear regressions. That is why, Monte Carlo simulations are a powerful
tool to model biochemical processes. |
|---|